Blind signal separation: statistical principles
Jean-François Cardoso
- Vol. 86, Iss: 10, pp 2009-2025
TLDR
The objectives of this paper are to review some of the approaches that have been developed to address blind signal separation and independent component analysis, to illustrate how they stem from basic principles, and to show how they relate to each other.Abstract:
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or "sources" from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach, but it requires us to venture beyond familiar second order statistics, The objectives of this paper are to review some of the approaches that have been developed to address this problem, to illustrate how they stem from basic principles, and to show how they relate to each other.read more
Citations
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Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
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Performance measurement in blind audio source separation
TL;DR: This paper considers four different sets of allowed distortions in blind audio source separation algorithms, from time-invariant gains to time-varying filters, and derives a global performance measure using an energy ratio, plus a separate performance measure for each error term.
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Natural gradient works efficiently in learning
TL;DR: In this paper, the authors used information geometry to calculate the natural gradients in the parameter space of perceptrons, the space of matrices (for blind source separation), and the spaces of linear dynamical systems for blind source deconvolution, and proved that Fisher efficient online learning has asymptotically the same performance as the optimal batch estimation of parameters.
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Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources
TL;DR: An extension of the infomax algorithm of Bell and Sejnowski (1995) is presented that is able blindly to separate mixed signals with sub- and supergaussian source distributions and is effective at separating artifacts such as eye blinks and line noise from weaker electrical signals that arise from sources in the brain.
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Kernel methods in machine learning
TL;DR: A review of machine learning methods employing positive definite kernels, ranging from binary classifiers to sophisticated methods for estimation with structured data, which include nonlinear functions as well as functions defined on nonvectorial data.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Journal ArticleDOI
An information-maximization approach to blind separation and blind deconvolution
TL;DR: It is suggested that information maximization provides a unifying framework for problems in "blind" signal processing and dependencies of information transfer on time delays are derived.
Journal ArticleDOI
Independent component analysis, a new concept?
TL;DR: An efficient algorithm is proposed, which allows the computation of the ICA of a data matrix within a polynomial time and may actually be seen as an extension of the principal component analysis (PCA).
Journal ArticleDOI
A fast fixed-point algorithm for independent component analysis
Aapo Hyvärinen,Erkki Oja +1 more
TL;DR: A novel fast algorithm for independent component analysis is introduced, which can be used for blind source separation and feature extraction, and the convergence speed is shown to be cubic.
Journal ArticleDOI
Blind beamforming for non-gaussian signals
TL;DR: In this paper, a computationally efficient technique for blind estimation of directional vectors, based on joint diagonalization of fourth-order cumulant matrices, is presented for beamforming.